Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

Development and Performance Investigation of a Unique Dual-rotor Savonius-type Counter-rotating Wind Turbine

Wind power is sustainable and prevalent virtually all over the globe. However, the conversion efficiency of the conventional single-rotor wind turbine (SRWT) is still far from satisfactory. The dual-rotor counter-rotating concept is among the reliable techniques used to enhance the efficiency of a wind energy conversion device for its renowned effectiveness. This study aims to investigate the performance of a Savonius dual/twin-rotor system, particularly in low-speed wind conditions while employing the counter-rotating technique. The evaluation of this technique is presented in terms of aerodynamic characteristics, including the power and torque coefficients. The results have shown that the new concept was able to improve the performance of the system extensively and was capable of operating in a lower wind speed condition. Compared to a single-rotor system, an additional 42% more torque was possible owing to the existence of a second rotor in the new system. The results have also revealed that the conversion efficiency of the system has been enhanced substantially. A corresponding average power coefficient of up to 28% was achieved. The present technique is thought to be promising for wind energy conversion systems, including sites with poor wind conditions.

Improvement of ASTM A53 Steel Durability Using Agrowastes as Carburizing Agent

The importance of steel in manufacturing and construction over the past century cannot be over-emphasized and easy accessibility couple with excellent mechanical properties make it preferable over others. However, the problem of durability has posed a serious concern as majority of steel application are meant for long term use. Several attempts have been made to improve the durability of steel in the past and increase of carbon content in low carbon steel was found to be a suitable agent. Although getting carbon is not the challenge rather obtaining it from a sustainable source that has zero environment impact. This research identified two separate agrowaste that has high carbon content the issue of sustainability brought about the development of carburizing agent from agrowaste that are easily accessible namely palm kernel and eggshell which is employed in this research. The use of agrowaste was found to be effective as there was notable increase in grain structure of the carburized steel when compared to the control sample without carburized agent in it.

Integrated Measurement System of Postural Angle and Electromyography Signal for Manual Materials Handling Assessment

Improper design of manual materials handling (MMH) tasks at workplace can cause musculoskeletal disorders such as muscle strain to industrial workers. To avoid these disorders, ergonomists and engineers require an integrated measurement system which allows them to study the interaction of body posture and muscle effort during performing MMH tasks. However, far too little attention has been paid to develop an integrated measurement system of body posture and muscle activity for assessing MMH tasks. The aim of this study was to develop and test a prototype of integrated system for measuring postural angles and electromyography (EMG) signals of a worker who doing MMH tasks. The Microsoft Visual Studio software, a 3D camera (Microsoft Kinect), Advancer Technologies muscle sensors and a microcontroller (NI DAQ USB-6000) were applied to develop the integrated postural angle and EMG signal measurement system. Additionally, a graphical user interface was created in the system to enable users to perform body posture and muscle effort assessment simultaneously. Based on the testing results, this study concluded that the patterns of EMG signals are depending on the postural angles which consistent with the findings of established works. Further study is required to enhance the validity, reliability and usability of the prototype so that it may facilitate ergonomists and engineers to assess work posture and muscle activity during MMH task.

Influence of Milling Process Parameters on Machined Surface Quality of Carbon Fibre Reinforced Polymer (CFRP) Composites Using Taguchi Analysis and Grey Relational Analysis

The article presents the milled surface quality of Uni-Directional Carbon Fibre Reinforced Polymer (UD-CFRP) composites from Taguchi’s and grey relational analysis. The novelty is demonstrating the possibility of detecting the surface defects in polymer composites during milling using SEM analysis. The material used for this study is UD-CFRP composite laminates and made by hand-layup process. All the milling operations were carried out using a solid tungsten carbide end milling tool and experiments conducted on CNC milling machine. Taguchi L9, 3-level orthogonal array was considered for experimentation. Analysis of Variance (ANOVA) was conducted to explore the significance of each individual input process parameters on multiple performance characteristics. Optimal process parameters are thoroughly validated by grey relational grade achieved by the grey relational analysis for multi performance characteristics. Finally, experimental results were correlated and analyzed with scanning electron micrographs using Scanning Electron Microscope (SEM).

Recognition of Human Emotion using Radial Basis Function Neural Networks with Inverse Fisher Transformed Physiological Signals

Emotion is a complex state of human mind influenced by body physiological changes and interdependent external events thus making an automatic recognition of emotional state a challenging task. A number of recognition methods have been applied in recent years to recognize human emotion. The motivation for this study is therefore to discover a combination of emotion features and recognition method that will produce the best result in building an efficient emotion recognizer in an affective system. We introduced a shifted tanh normalization scheme to realize the inverse Fisher transformation applied to the DEAP physiological dataset and consequently performed series of experiments using the Radial Basis Function Artificial Neural Networks (RBFANN). In our experiments, we have compared the performances of digital image based feature extraction techniques such as the Histogram of Oriented Gradient (HOG), Local Binary Pattern (LBP) and the Histogram of Images (HIM). These feature extraction techniques were utilized to extract discriminatory features from the multimodal DEAP dataset of physiological signals. Experimental results obtained indicate that the best recognition accuracy was achieved with the EEG modality data using the HIM features extraction technique and classification done along the dominance emotion dimension. The result is very remarkable when compared with existing results in the literature including deep learning studies that have utilized the DEAP corpus and also applicable to diverse fields of engineering studies.

Thermal Modelling and Analysis of A 10HP Induction Machine Using the Lumped Parameter Approach

The essence of this research work is to develop a thermal model for an induction machine that will enable the prediction of temperature in different parts of the machine. This is very important first to the manufacturer or designer of an induction machine because with these predictions one can decide on the insulation class limits the machine belongs to. Also modern trends in the construction of machines is moving in the direction of making machines with reduced weights, costs and increased efficiency. In order to achieve this, the thermal analysis becomes very crucial in deciding on what types of insulators and other materials that would be used to make these machines. In industries, the knowledge of the thermal limits of machines if well utilized increases the life span of the machines and reduces downtime; thereby increasing production and profit. Specifically, this paper (i) predicted the temperature limits of the induction machine and its components, (ii) developed an accurate thermal model for an induction machine, (iii) predicted the temperature in different parts of the induction machine using the thermal model and software program and lastly (iv) investigated how the machine symmetry is affected by the nodal configuration.

A Simulation Study of Drying Chamber for Marine Product

Drying chamber is a drying application for agriculture product to produce high quality and hygiene product. The purpose of this paper is to propose best configuration trays arrangement in drying chamber for better distribution of velocity and temperature. Therefore, five configurations of trays are analyzed to obtain the best performance of uniformity air flow distribution within drying chamber. CFD simulation studied the uniform air flow in the drying chamber in steady state condition. A validation is performed by comparing the data obtained from the literature review CFD simulation to ensure the methodology is correct. Then, the drying chamber with different trays arrangements are simulated using CFD simulations to obtain velocity and temperature distributions at nine plotted points on trays. From the results obtained, it concluded that design (A) and (D) are selected as the best designs for uniformity because there is less discrepancy for each point contributed the more uniformity of distribution.

Design of a 1.9 GHz low-power LFSR Circuit using the Reed-Solomon Algorithm for Pseudo-Random Test Pattern Generation

A linear feedback shift register (LFSR) has been frequently used in the Built-in Self-Test (BIST) designs for the pseudo-random test pattern generation. The higher volume of the test patterns and the lower test power consumption are the key features in the large complex designs. The motivation of this study is to generate efficient pseudo-random test patterns by the proposed LFSR and to be applied in the BIST designs. For the BIST designs, the proposed LFSR satisfied with the main strategies such as re-seeding and lesser test power consumption. However, the reseeding approach was utilized by the maximum-length pseudo-random test patterns. The objective of this paper is to propose a new LFSR circuit based on the proposed Reed-Solomon (RS) algorithm. The RS algorithm is created by considering the factors of the maximum length test patterns with a minimum distance over the time t. Also, it has been achieved an effective generation of test patterns over a stage of complexity order O (m log2 m), where m denotes the total number of message bits. We analysed our RS LFSR mathematically using the feedback polynomial function to decrease the area overhead occupied in the designs. The simulation works of the proposed RS LFSR bit-wise stages are simulated using the TSMC 130 nm on the Mentor Graphics IC design platform. Experimental results showed that the proposed LFSR achieved the effective pseudo-random test patterns with a lower test power consumption of 25.13 µW and 49.9 µs. In addition, proposed LFSR along with existing authors’ LFSR are applied in the BIST design to examine their power consumption. Ultimately, overall simulations operated with the highest operating frequency environment as 1.9 GHz.

Tear Resistance and Morphology Study of Tree Pruning Waste Papers: Effect of Soda Pulping Concentration

With the aim to explore the use of tree pruning waste as replacement material in papermaking, a study was conducted to investigate the effect of soda pulping concentration on the tear resistance and surface morphology of the fabricated papers. By varying the sodium hydroxide concentration from 5% to 25%, tree pruning waste papers with different tear resistance and surface morphology were fabricated. The tree pruning waste papers with the optimum tear resistance (73049.68 mN) was produced when the pulping medium was prepared at 20% sodium hydroxide concentration. As confirmed by the morphology study, the pulp fibres with improved interlocking surface morphology was produced at 20% sodium hydroxide concentration. Beyond that, fibre fibrillation had took place and exerted negative impact on the tear resistance of the papers. The present study confirms the use of tree pruning waste as an alternative in papermaking. Nonetheless, the soda pulping concentration must be properly regulated in order to maximize the performance of the fabricated paper products.